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Kolla S, Falakshahi H, Abrol A, Fu Z, Calhoun VD. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer's Disease and Cognitive Impairment. Sensors (Basel) 2024; 24:814. [PMID: 38339531 PMCID: PMC10857295 DOI: 10.3390/s24030814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/10/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Network neuroscience, a multidisciplinary field merging insights from neuroscience and network theory, offers a profound understanding of neural network intricacies. However, the impact of varying node sizes on computed graph metrics in neuroimaging data remains underexplored. This study addresses this gap by adopting a data-driven methodology to delineate functional nodes and assess their influence on graph metrics. Using the Neuromark framework, automated independent component analysis is applied to resting state fMRI data, capturing functional network connectivity (FNC) matrices. Global and local graph metrics reveal intricate connectivity patterns, emphasizing the need for nuanced analysis. Notably, node sizes, computed based on voxel counts, contribute to a novel metric termed 'node-metric coupling' (NMC). Correlations between graph metrics and node dimensions are consistently observed. The study extends its analysis to a dataset comprising Alzheimer's disease, mild cognitive impairment, and control subjects, showcasing the potential of NMC as a biomarker for brain disorders. The two key outcomes underscore the interplay between node sizes and resultant graph metrics within a given atlas, shedding light on an often-overlooked source of variability. Additionally, the study highlights the utility of NMC as a valuable biomarker, emphasizing the necessity of accounting for node sizes in future neuroimaging investigations. This work contributes to refining comparative studies employing diverse atlases and advocates for thoughtful consideration of intra-atlas node size in shaping graph metrics, paving the way for more robust neuroimaging research.
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Affiliation(s)
- Sahithi Kolla
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA 30303, USA; (S.K.); (A.A.); (Z.F.)
- Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
| | - Haleh Falakshahi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA 30303, USA; (S.K.); (A.A.); (Z.F.)
- Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA 30303, USA; (S.K.); (A.A.); (Z.F.)
- Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA 30303, USA; (S.K.); (A.A.); (Z.F.)
- Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA 30303, USA; (S.K.); (A.A.); (Z.F.)
- Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
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Falakshahi H, Rokham H, Miller R, Liu J, Calhoun VD. Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data . Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-6. [PMID: 38083176 DOI: 10.1109/embc40787.2023.10340856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Multimodal brain network analysis has the potential to provide insights into the mechanisms of brain disorders. Most previous studies have analyzed either unimodal brain graphs or focused on local/global graphic metrics with little consideration of details of disrupted paths in the patient group. As we show, the combination of multimodal brain graphs and disrupted path-based analysis can be highly illuminating to recognize path-based disease biomarkers. In this study, we first propose a way to estimate multimodal brain graphs using static functional network connectivity (sFNC) and gray matter features using a Gaussian graphical model of schizophrenia versus controls. Next, applying the graph theory approach we identify disconnectors or connectors in the patient group graph that create additional paths or cause absent paths compared to the control graph. Results showed several edges in the schizophrenia group graph that trigger missing or additional paths. Identified edges associated with these disrupted paths were identified both within and between dFNC and gray matter which highlights the importance of considering multimodal studies and moving beyond pairwise edges to provide a more comprehensive understanding of brain disorders.Clinical Relevance- We identified a path-based biomarker in schizophrenia, by imitating the structure of paths in a multimodal (sMIR+fMRI) brain graph of the control group. Identified cross-modal edges associated with disrupted paths were related to the middle temporal gyrus and cerebellar regions.
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Rokham H, Falakshahi H, Calhoun VD. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38082903 DOI: 10.1109/embc40787.2023.10339949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings are bounded by the limitation of each modality. In addition, the lack of validity of current psychosis nosology created more complications in understanding biomarkers. In this study, we introduced a deep convolutional framework to classify and identify label noises using structural and functional magnetic resonance imaging data. We applied our method to functional and structural MRI data from a schizophrenia dataset and evaluated the model's performance in a cross-validated form. In addition, we introduced a noise criterion to distinguish a potentially noisy subject for each modality. Our results show the learned model using resting-state functional MRI data is more informative and has higher performance in comparison with structural MRI data. Lastly, based on the noise level, we investigated potential borderline subjects as possible subtypes and made a statistical analysis to distinguish differences between resting-state static functional connectivity features.Clinical Relevance- Results show schizophrenia patients are separable from the healthy control group based on their neuroimaging data and resting-state functional MRI data is more informative than structural MRI data and hence contains less label noise.
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Rokham H, Falakshahi H, Fu Z, Pearlson G, Calhoun VD. Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification. Hum Brain Mapp 2023; 44:3180-3195. [PMID: 36919656 PMCID: PMC10171526 DOI: 10.1002/hbm.26273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.
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Affiliation(s)
- Hooman Rokham
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
| | - Haleh Falakshahi
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
| | - Godfrey Pearlson
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
- Department of NeuroscienceYale UniversityNew HavenConnecticutUSA
- Olin Neuropsychiatry Research CenterHartford HospitalHartfordConnecticutUSA
| | - Vince D. Calhoun
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
- Department of PsychologyGeorgia State UniversityAtlantaGeorgiaUSA
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Falakshahi H, Rokham H, Fu Z, Iraji A, Mathalon DH, Ford JM, Mueller BA, Preda A, van Erp TGM, Turner JA, Plis S, Calhoun VD. Path Analysis: A Method to Estimate Altered Pathways in Time-varying Graphs of Neuroimaging Data. Netw Neurosci 2022; 6:634-664. [PMID: 36204419 PMCID: PMC9531579 DOI: 10.1162/netn_a_00247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022] Open
Abstract
Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.
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Affiliation(s)
- Haleh Falakshahi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hooman Rokham
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Judith M. Ford
- Department of Psychiatry, University of California, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Bryon A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Jessica A. Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Sergey Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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Falakshahi H, Vergara VM, Liu J, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, McEwen S, Potkin SG, Preda A, Rokham H, Sui J, Turner JA, Plis S, Calhoun VD. Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia. IEEE Trans Biomed Eng 2020; 67:2572-2584. [PMID: 31944934 PMCID: PMC7538162 DOI: 10.1109/tbme.2020.2964724] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). METHODS We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method. RESULTS Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components. CONCLUSION We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality. SIGNIFICANCE The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
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Rokham H, Pearlson G, Abrol A, Falakshahi H, Plis S, Calhoun VD. Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders. Biol Psychiatry Cogn Neurosci Neuroimaging 2020; 5:819-832. [PMID: 32771180 PMCID: PMC7760893 DOI: 10.1016/j.bpsc.2020.05.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 05/04/2020] [Accepted: 05/06/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Mental health diagnostic approaches are seeking to identify biological markers to work alongside advanced machine learning approaches. It is difficult to identify a biological marker of disease when the traditional diagnostic labels themselves are not necessarily valid. METHODS We worked with T1 structural magnetic resonance imaging data collected from 1493 individuals comprising healthy control subjects, patients with psychosis, and their unaffected first-degree relatives. Specifically, the dataset included 176 bipolar disorder probands, 134 schizoaffective disorder probands, 240 schizophrenia probands, 362 control subjects, and 581 patient relatives. We assumed that there might be noise in the diagnostic labeling process. We detected label noise by classifying the data multiple times using a support vector machine classifier, and then we flagged those individuals in which all classifiers unanimously mislabeled those subjects. Next, we assigned a new diagnostic label to these individuals, based on the biological data (magnetic resonance imaging), using an iterative data cleansing approach. RESULTS Simulation results showed that our method was highly accurate in identifying label noise. Both diagnostic and biotype categories showed about 65% and 63% of noisy labels, respectively, with the largest amount of relabeling occurring between the healthy control subjects and individuals with bipolar disorder and schizophrenia as well as in unaffected close relatives. The extraction of imaging features highlighted regional brain changes associated with each group. CONCLUSIONS This approach represents an initial step toward developing strategies that need not assume that existing mental health diagnostic categories are always valid but rather allows us to leverage this information while also acknowledging that there are misassignments.
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Affiliation(s)
- Hooman Rokham
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia; Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University, New Haven, Connecticut; Department of Neuroscience, Yale University, New Haven, Connecticut; Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, Connecticut
| | - Anees Abrol
- Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia
| | - Haleh Falakshahi
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia; Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia
| | - Sergey Plis
- Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia; Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia; Department of Psychology, Georgia State University, Atlanta, Georgia; Department of Psychiatry, Yale University, New Haven, Connecticut
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